ACR–ASTRO Practice Parameter for the Performance of Stereotactic Body Radiation Therapy
Bibliographic record
Abstract
AIM/OBJECTIVES/BACKGROUND: To standardize the practice of stereotactic body radiation therapy (SBRT), the American College of Radiology (ACR) and the American Society for Radiation Oncology (ASTRO) cooperatively developed the practice parameter for SBRT. SBRT is a treatment technique that delivers radiation dose to a well-defined extracranial target in 5 fractions or less and usually employs a higher dose per fraction than used in conventional radiation. METHODS: The ACR-ASTRO Practice Parameter for the Performance of Stereotactic Body Radiation Therapy was revised according to the process described on the ACR website ("The Process for Developing ACR Practice Parameters and Technical Standards," www.acr.org/ClinicalResources/Practice-Parameters-and-Technical-Standards) by the Committee on Practice Parameters of the ACR Commission on Radiation Oncology in collaboration with the ASTRO. Both societies then reviewed and approved the document. RESULTS: Given the complexities of SBRT, a separate document was created to develop a technical standard for the medical physics of SBRT (ACR-AAPM Technical Standard for Medical Physics Performance Monitoring of Stereotactic Body Radiation Therapy). Workflow, qualifications and responsibilities of personnel, specifications, documentation, quality control/safety/improvement, simulation/treatment, and follow-up were addressed in this practice parameter. CONCLUSIONS: This practice parameter assists practitioners in providing safe and appropriate SBRT treatment and care for patients when clinically indicated. As technologies and techniques continue to evolve, this document will be reviewed, revised and renewed accordingly to a 5 year or sooner timeline specified by the ACR.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.047 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.021 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".